Since the start of the COVID era, when people were prevented from eating at restaurants for long periods of time, consumers around the world have turned to restaurant ordering and delivery to place food on the table for themselves and their families. I have become more and more dependent on apps.
To address the upheaval in food consumption dynamics, Hmmm!brandOur digital and technology team has invested heavily in developing or enhancing our app for restaurants. Kentucky Fried Chicken, pizza hut, taco bellWhen The Habit Burger Grill.
The concept of having a restaurant ordering app was relatively new, especially for KFC in the US. To encourage KFC customers to download and use the app, they had to make sure it was “relevant, easy, and distinctive.” This was RED, as former CEO Greg Creed liked to say.
But I needed metrics to make sure it was really RED. I needed to know if the app really made the fried chicken ordering process easy. Were people happy with the app? Were there recurring patterns among customers who liked (or didn’t like) the app? Did certain app release versions perform better than others? Did you
They were among the questions we had to find answers to. Both Apple and Android have access to consumer ratings and reviews, but they can’t dig deep into what reviews mean for their products. So we turned to Domo and the tools that have become our secret sauce. Jupyter workspace.
Jupyter Workspaces allows you to access and analyze this qualitative data. In my experience with other business intelligence platforms, text analytics were limited to word counts and word clouds.
Meanwhile, Jupyter Workspaces take text analytics to the next level, allowing practitioners to combine Python’s advanced Natural Language Processing (NLP) capabilities with datasets within Domo. You can also schedule Jupyter Notebooks as DataFlows to automatically refresh data. By using Python and Domo together, KFC can now:
| python | Domo |
| Directly import customer reviews from Apple and Android stores and combine them into one dataset | Schedule a Jupyter Notebook to automatically update daily |
| Uses natural language processing models to identify customer sentiment towards your app in each review | Create datasets that can be shared across your organization |
| Extract key metrics such as when reviews were written and user star rating | Use company branding and interactive visuals to explain your results and metrics in an engaging way |
All of these features help KFC’s mobile app team gain insight. Today, the team can identify what works and what doesn’t for customers, and foster ideas for future app improvements. All of this shows that when KFC customers speak, we listen.
